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| 1 | From Parallel Plants to Smart Plants:Intelligent Control and Management for Plant Growth显示文摘Precision management of agricultural systems, aiming at optimizing profitability, productivity and sustainability,comprises a set of technologies including sensors, information systems, and informed management, etc. Expert systems are expected to aid farmers in plant management or environment control, but they are mostly based on the offline and static information, deviated from the actual situation. Parallel management,achieved by virtual/artificial agricultural system, computational experiment and parallel execution, provides a generic framework of solution for online decision support. In this paper, we present the three steps toward the parallel management of plant: growth description(the crop model), prediction, and prescription. This approach can update the expert system by adding learning ability and the adaption of knowledge database according to the descriptive and predictive model. The possibilities of passing the knowledge of experienced farmers to younger generation, as well as the application to the parallel breeding of plant through such system, are discussed. | Mengzhen Kang Fei-Yue Wang | 2017 | IEEE/CAA Journal of Automatica Sinica2017，4，2： | 25 |
| 2 | Effect of streambed sediment on benthic ecology显示文摘Benthic macroinvertebrates have been commonly used as indicator species for assessment of aquatic ecology. Streambed sediment, or substrate, plays an important role in habitat conditions for macroinvertebrate communities. Field investigations were done to study the benthic diversity and macroinvertebrate compositions in various stream substrata. Sampling sites with different bed sediment, latitude, and climate were selected along the Yangtze River, the Yellow River, the East River, and the Juma River, in China. The results show that benthic community structures found in different substrata clearly differ, while those found in substrata of similar composition and flow conditions but in different macroclimates are similar. The study, thus, demonstrates that the benthic macroinvertebrate community is mainly affected by substrate composition and flow conditions, but is generally unaffected by latitudinal position and macroclimate. Taxa richness of the macroinvertebrate community was found to be the highest on hydrophyte-covered cobbles, high on moss-covered bedrock, and low on clay beds and cobble beds devoid of plant biomass. Sandy beds are compact and unstable, thus, no benthic macroinvertebrates were found colonizing such substrata. Aquatic insects account for most of the macroinvertebrates collected in these rivers. Different insects dominate in different types of substrata: mainly EPT species (Ephemeroptera, Plecoptera, Trichoptera) in cobble, gravel, and moss-covered bedrock; and Chironomidae larvae in clay beds. The relation between the number of species in the samples and the size of the sampling area fits a power function of the species area. One square meter (1m2) is suggested as the minimum sampling area. A substrate suitability index is proposed by integrating the suitability of sediment, periphyton, and benthic organic materials for macroinvertebrates. The biodiversity of macroinvertebrates increases linearly with the substrate suitability index. Benthic taxa richness increases linearly with the suitability index. | Xuehua DUAN Zhao-Yin WANG Mengzhen XU Kang ZHANG | 2009 | International Journal of Sediment Research2009，24，3： | 12 |
| 3 | DeCASA in AgriVerse: Parallel Agriculture for Smart Villages in Metaverses显示文摘The demand for food is tremendously increasing with the growth of the world population,which necessitates the development of sustainable agriculture under the impact of various factors,such as climate change.To fulfill this challenge,we are developing Metaverses for agriculture,referred to as Agri Verse,under our Decentralized Complex Adaptive Systems in Agriculture(De CASA)project,which is a digital world of smart villages created alongside the development of Decentralized Sciences(De Sci)and Decentralized Autonomous Organizations(DAO)for Cyber-Physical-Social Systems(CPSSs).Additionally,we provide the architectures,operating modes and major applications of De CASA in AgriVerse.For achieving sustainable agriculture,a foundation model based on ACP theory and federated intelligence is envisaged.Finally,we discuss the challenges and opportunities. | Xiujuan Wang Mengzhen Kang Hequan Sun Philippe de Reffye Fei-Yue Wang | 2022 | IEEE/CAA Journal of Automatica Sinica2022，9，12： | 4 |
| 4 | Can Digital Intelligence and Cyber-Physical-Social Systems Achieve Global Food Security and Sustainability?显示文摘Plants sequester carbon through photosynthesis and provide primary productivity for the ecosystem. However, they also simultaneously consume water through transpiration, leading to a carbon-water balance relationship. Agricultural production can be regarded as a form of carbon sequestration behavior.From the perspective of the natural-social-economic complex ecosystem, excessive water usage in food production will aggravate regional water pressure for both domestic and industrial purposes. Hence, achieving a harmonious equilibrium between carbon and water resources during the food production process is a key scientific challenge for ensuring food security and sustainability. Digital intelligence(DI) and cyber-physical-social systems(CPSS) are emerging as the new research paradigms that are causing a substantial shift in the conventional thinking and methodologies across various scientific fields, including ecological science and sustainability studies. This paper outlines our recent efforts in using advanced technologies such as big data, artificial intelligence(AI), digital twins, metaverses, and parallel intelligence to model, analyze, and manage the intricate dynamics and equilibrium among plants, carbon, and water in arid and semiarid ecosystems. It introduces the concept of the carbon-water balance and explores its management at three levels: the individual plant level, the community level, and the natural-social-economic complex ecosystem level. Additionally, we elucidate the significance of agricultural foundation models as fundamental technologies within this context. A case analysis of water usage shows that, given the limited availability of water resources in the context of the carbon-water balance, regional collaboration and optimized allocation have the potential to enhance the utilization efficiency of water resources in the river basin. A suggested approach is to consider the river basin as a unified entity and coordinate the relationship between the upstream, midstream and downstream areas. Furthermore, establishing mechanisms for water resource transfer and trade among different industries can be instrumental in maximizing the benefits derived from water resources.Finally, we envisage a future of agriculture characterized by the integration of digital, robotic and biological farming techniques.This vision aims to incorporate small tasks, big models, and deep intelligence into the regular ecological practices of intelligent agriculture. | Yanfen Wang Mengzhen Kang Yali Liu Juanjuan Li Kai Xue Xiujuan Wang Jianqing Du Yonglin Tian Qinghua Ni Fei-Yue Wang | 2023 | IEEE/CAA Journal of Automatica Sinica2023，10，11： | 1 |
| 5 | A stochasticmodel of tree architecture and biomass partitioning:application to Mongolian Scots pines 显示文摘 | Wang Feng Kang Mengzhen Lu Qi | 2011 | Annals ofBotany2011，107，5： | 1 |
| 6 | Fast construction of geometrical structure of plant with substructures algorithm显示文摘 | Kang Mengzhen De Reffye P Hu Baogang | 2004 | Journal of Image and Graphics2004，9，1： | 1 |
| 7 | A dynamic, architectural plant model simulating resource- dependent growth显示文摘 | Yah Hongping Kang Mengzhen De Reffye P | 2000 | Annals of Botany2000，4，93： | 1 |
| 8 | A dynamic, architectural plant model simulating resource-dependent growth显示文摘 | Yan Hongping Kang Mengzhen de Reffye P Dingkuhn M | 2004 | Annals of Botany2004，93，5： | 1 |
| 9 | A Novel Agricultural Data Sharing Mode Based on Rice Disease Identification显示文摘In this paper,a variety of classical convolutional neural networks are trained on two different datasets using transfer learning method.We demonstrated that the training dataset has a significant impact on the training results,in addition to the optimization achieved through the model structure.However,the lack of open-source agricultural data,combined with the absence of a comprehensive open-source data sharing platform,remains a substantial obstacle.This issue is closely related to the difficulty and high cost of obtaining high-quality agricultural data,the low level of education of most employees,underdeveloped distributed training systems and unsecured data security.To address these challenges,this paper proposes a novel idea of constructing an agricultural data sharing platform based on a federated learning(FL)framework,aiming to overcome the deficiency of high-quality data in agricultural field training. | Mengmeng ZHANG Xiujuan WANG Mengzhen KANG Jing HUA Haoyu WANG Feiyue WANG | 2024 | Plant Diseases and Pests2024，15，2： | 0 |
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